Bottom Line:
The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy.A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary.A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

The detection of the pectoral muscle boundary in the medio-lateral oblique view of mammograms is essential to improving the computer-aided diagnosis of breast cancer. In this study, a shape-based detection method is proposed for accurately extracting the boundary of the pectoral muscle in mammograms. A shape-based enhancement mask is applied to the mammogram and the initial boundary is then defined using morphological operators. The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy. A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary. The proposed method was applied to 322 mammograms from the mini Mammographic Image Analysis Society database. A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

Mentions:
The horizontal pixels around the currently processed center are considered more in the SBEM with a 7 × 2 pixel kernel. Therefore, the pectoral muscles obscured by sticky tape can be well detected, as shown in Fig. 6a, since the intensity changes still exist in the horizontal orientation. Sometimes pectoral muscle has several layers and the inside lines can easily confuse the detection of the true edge. In the proposed method, the initial boundary is thus selected based on the prior knowledge of the pectoral muscle’s relatively high gray intensity level and location. In the segmented initial boundaries acquired from a mammogram consisting of multiple layers, the average intensity value of each layer is calculated. If these average intensity values are roughly equal, the right layer is chosen as the initial boundary based on which start point is defined. Otherwise, the edge with the highest average intensity value is regarded as the initial boundary for finding the start point. Satisfactory results (Figs. 6(b, c) are obtained using this method. The proposed shape-based growth strategy has some strong advantages. The detection results are not affected by the size of the pectoral muscle (small (Fig. 6d) or large (Fig. 6e), since pectoral muscle boundary growth depends greatly on the start point and stops when the end condition is satisfied. The upper edge also extends smoothly to the left according to the candidates designed in the shape-based growth mask, which reduces the disturbance of the dense tissue on the lower half of the pectoral muscle, producing well segmented edges (Fig. 6f, g). Furthermore, the shape-based growth mask does not set a fixed shape of the edge for growth. The edge grows well from the start point whether the pectoral muscle edge is similar to a vertical line (Fig. 6h) or is a fuzzy texture with complex curvature (Fig. 6i). However, during the initial boundary detection using the proposed method, some cases fail when the pectoral muscle has more than two layers and the inner layers have higher intensities than that of the surface layer. For these cases, the inner line would be chosen as the initial edge. The extraction of the edge in Fig. 6(j) (mdb 223) is poor because the start point on the acquired initial boundary is on the second layer of the pectoral muscle. When the upper part of the pectoral muscle is covered by other tissues and no obvious start point exists, invalid results are often obtained (Fig. 6k) (mdb 183). Figure 7 compares the proposed method and existing methods. Because the subimages are different, the fields of view have some differences. Figure 7(a) displays failed detection of mdb061 processed by Kwok and Fig. 7(c) is the inaccurate detection of mdb053 published by Chakraborty respectively. Figures 7(b,d) show the correct edges obtained using the proposed method.Fig. 7

Mentions:
The horizontal pixels around the currently processed center are considered more in the SBEM with a 7 × 2 pixel kernel. Therefore, the pectoral muscles obscured by sticky tape can be well detected, as shown in Fig. 6a, since the intensity changes still exist in the horizontal orientation. Sometimes pectoral muscle has several layers and the inside lines can easily confuse the detection of the true edge. In the proposed method, the initial boundary is thus selected based on the prior knowledge of the pectoral muscle’s relatively high gray intensity level and location. In the segmented initial boundaries acquired from a mammogram consisting of multiple layers, the average intensity value of each layer is calculated. If these average intensity values are roughly equal, the right layer is chosen as the initial boundary based on which start point is defined. Otherwise, the edge with the highest average intensity value is regarded as the initial boundary for finding the start point. Satisfactory results (Figs. 6(b, c) are obtained using this method. The proposed shape-based growth strategy has some strong advantages. The detection results are not affected by the size of the pectoral muscle (small (Fig. 6d) or large (Fig. 6e), since pectoral muscle boundary growth depends greatly on the start point and stops when the end condition is satisfied. The upper edge also extends smoothly to the left according to the candidates designed in the shape-based growth mask, which reduces the disturbance of the dense tissue on the lower half of the pectoral muscle, producing well segmented edges (Fig. 6f, g). Furthermore, the shape-based growth mask does not set a fixed shape of the edge for growth. The edge grows well from the start point whether the pectoral muscle edge is similar to a vertical line (Fig. 6h) or is a fuzzy texture with complex curvature (Fig. 6i). However, during the initial boundary detection using the proposed method, some cases fail when the pectoral muscle has more than two layers and the inner layers have higher intensities than that of the surface layer. For these cases, the inner line would be chosen as the initial edge. The extraction of the edge in Fig. 6(j) (mdb 223) is poor because the start point on the acquired initial boundary is on the second layer of the pectoral muscle. When the upper part of the pectoral muscle is covered by other tissues and no obvious start point exists, invalid results are often obtained (Fig. 6k) (mdb 183). Figure 7 compares the proposed method and existing methods. Because the subimages are different, the fields of view have some differences. Figure 7(a) displays failed detection of mdb061 processed by Kwok and Fig. 7(c) is the inaccurate detection of mdb053 published by Chakraborty respectively. Figures 7(b,d) show the correct edges obtained using the proposed method.Fig. 7

Bottom Line:
The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy.A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary.A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

The detection of the pectoral muscle boundary in the medio-lateral oblique view of mammograms is essential to improving the computer-aided diagnosis of breast cancer. In this study, a shape-based detection method is proposed for accurately extracting the boundary of the pectoral muscle in mammograms. A shape-based enhancement mask is applied to the mammogram and the initial boundary is then defined using morphological operators. The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy. A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary. The proposed method was applied to 322 mammograms from the mini Mammographic Image Analysis Society database. A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.